Verifying and correcting recognition string hypotheses using discriminative utterance verification

Sukkar, R.A.; Setlur, A.R.; Lee, Chin‐Hui; Jacob, J.

doi:10.1016/s0167-6393(97)00031-9

Cited by 22 publications

(6 citation statements)

References 22 publications

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“…The above models K T and K C can be estimated based on different criteria, such as maximum likelihood (ML), or minimum verification error (MVE), etc. shows the ML-trained models already significantly surpass the conventional UV methods, such as in (Sukkar and Lee, 1996;Sukkar et al, 1997;Rahim et al, 1997;Rahim and Lee, 1997a).…”

Section: In-search Data Selection For Accurate Competing Modelsmentioning

confidence: 96%

“…In , it is found that the minimum classification error (MCE) training, which is originally proposed to reduce recognition errors, can contribute to improving performance of UV. In (Rahim and Lee, 1997a;Sukkar et al, 1997), a GPD-based training algorithm is proposed to achieve minimum verification error (MVE) estimation for utterance verification with respect to optimizing verification HMM parameters. In MVE, the string-level verification errors are approximated by using a sigmoid function embedded with a misverification function, which actually is negative log-likelihood ratio used in verification.…”

Section: As Utterance Verificationmentioning

confidence: 99%

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Confidence measures for speech recognition: A survey

Jiang

2005

Speech Communication

308

163

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In speech recognition, confidence measures (CM) are used to evaluate reliability of recognition results. A good confidence measure can largely benefit speech recognition systems in many practical applications. In this survey, I summarize most research works related to confidence measures which have been done during the past 10-12 years. I will present all these approaches as three major categories, namely CM as a combination of predictor features, CM as a posterior probability, and CM as utterance verification. Then, I also introduce some recent advances in the area. Moreover, I will discuss capabilities and limitations of the current CM techniques and generally comment on todayÕs CM approaches. Based on the discussion, I will conclude the paper with some clues for future works.

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Section: In-search Data Selection For Accurate Competing Modelsmentioning

confidence: 96%

Section: As Utterance Verificationmentioning

confidence: 99%

Confidence measures for speech recognition: A survey

Jiang

2005

Speech Communication

308

163

View full text Add to dashboard Cite

show abstract

“…Train SpeakersGMMs using Set 12 ; Train WorldGMM using WorldSet 12 ; foreach TrainSession in (3,4,5,6) Calculate Thresholds using Set TrainSession , SpeakersGMMs, WorldGMM; foreach TestSession in (3,4,5,6) and TestSession =TrainSession Obtain %FA and %FR using Set TestSession , SpeakersGMMs, WorldGMM, Thresholds;…”

Section: A Priori Authenticationmentioning

confidence: 99%

“…One is based on verifying the actual content of the speech as a means of verifying the speaker identity [3][4][5]. This can be considered as the classical "password-based" approach and implies that the speech content must be kept secret and attached to the speaker.…”

Section: Introductionmentioning

confidence: 99%

On combining classifiers for speaker authentication

Rodríguez-Liñares

Garcı́a-Mateo

Alba-Castro

2003

Pattern Recognition

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Speaker Verification and Utterance Verification are examples of techniques that can be used for Speaker Authentication purposes.Speaker Verification consists of accepting or rejecting the claimed identity of a speaker by processing samples of his/her voice. Usually, these systems are based on HMM models that try to represent the characteristics of the speakers' vocal tracts.Utterance Verification systems make use of a set of speaker-independent speech models to recognize a certain utterance. If the utterances consist of passwords, this can be used for identity verification purposes.Up to now, both techniques have been used separately. This paper is focused on the problem of how to combine these two sources of information. New architectures are presented to join an utterance verification system and a speaker verification system in order to improve the performance in a speaker verification task.

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“…Here we use the same notation for the word and the corresponding model interchangeably without ambiguity. In practice, p(X u |w u ) is represented by some functional forms such as a linear combination of the likelihood values for several models, e.g., the background and the impostor models [2], or the anti-keyword and the filler models [5]. The legitimacy of SLR-based hypothesis testing depends on the conditional probability density function (pdf) being accurate for each word, and in the case of continuous speech recognition, it also depends on the condition that the acoustic token X u is correctly segmented.…”

Section: Introductionmentioning

confidence: 99%

Verification of Multi-Class Recognition Decision: A Classification Approach

Matsui¹,

Soong²,

Juang

2005

IEICE Transactions on Information and Systems

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We investigate strategies to improve the utterance verification performance using a 2-class pattern classification approach, including: utilizing N-best candidate scores, modifying segmentation boundaries, applying background and out-of-vocabulary filler models, incorporating contexts, and minimizing verification errors via discriminative training. A connected-digit database recorded in a noisy, moving car with a hands-free microphone mounted on the sun-visor is used to evaluate the verification performance. The equal error rate (EER) of word verification is employed as the sole performance measure. All factors and their effects on the verification performance are presented in detail. The EER is reduced from 29%, using the standard likelihood ratio test, down to 21.4%, when all features are properly integrated.

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Verifying and correcting recognition string hypotheses using discriminative utterance verification

Cited by 22 publications

References 22 publications

Confidence measures for speech recognition: A survey

Confidence measures for speech recognition: A survey

On combining classifiers for speaker authentication

Verification of Multi-Class Recognition Decision: A Classification Approach

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